GEOG324 Lab 6

Load packages

library(readxl)
library(tidyverse)

## Warning: package 'ggplot2' was built under R version 4.0.3

## Warning: package 'dplyr' was built under R version 4.0.3

## Warning: package 'forcats' was built under R version 4.0.3

library(sf)
library(raster)
library(tmap)
library(ggplot2)
library(plotly)
library(viridis)

Load data

load("depth_stack.RData")
hydros <- read_excel("hydros.xlsx")
webmercator <- "+proj=merc +a=6378137 +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0
+k=1 +units=m +nadgrids=@null +wktext +no_defs"

# Exercise 1
elevation <- raster("carlisle_LiDAR_15m.tif")
map <- tm_shape(elevation, name = "Elevation") +
  tm_raster(alpha = 0.2, palette = terrain.colors(10),
            style = "cont", title = "Elevation (m)")

maxDepth <- max(depth_stack, na.rm = TRUE)
maxTime <- raster::which.max(depth_stack)

crs(maxDepth) <- CRS('+init=EPSG:27700')
crs(maxTime) <- CRS('+init=EPSG:27700')
maxDepth <- projectRaster(maxDepth, crs = webmercator)
maxTime <- projectRaster(maxTime, crs = webmercator)


# Exercise 2
load("channel_depth.RData")
hydros$channel_depth <- channel_depth


# Exercise 3
mean_depth <- cellStats(depth_stack, stat = "mean")
max_depth <- cellStats(depth_stack, stat = "max")
load("total_area.RData")
flood_df <- tibble(mean_depth, max_depth, total_area)
flood_df$date <- hydros$DateTime


# Exercise 4
load("buildings_flooded.RData")
hydros$buildings_flooded <- buildings_flooded

Exercise 1 - Mapping max flood depth & time it occurred

tmap_mode("view")

## tmap mode set to interactive viewing

map +
  tm_shape(maxDepth) +
  tm_raster(palette = "Blues", alpha = 0.5) +
  tm_shape(maxTime) +
  tm_raster(palette = "Reds", alpha = 0.5)

Exercise 2 - Channel depth line plot

depth_line <- hydros %>%
  ggplot(mapping = aes(x = DateTime,
                       y = channel_depth)) +
  geom_line(col = "blue") +
  labs(title = "Channel Boundary Depth",
       x = "Time",
       y = "Channel Depth (m)")

ggplotly(depth_line)

Exercise 2 - Channel depth scatterplot

depth_scatter <- hydros %>%
  mutate(total = (Caldew + Petteril + Eden)) %>%
  ggplot(mapping = aes(x = total,
                       y = channel_depth,
                       col = DateTime)) +
  geom_point() +
  labs(title = "Total River Inflow vs. Channel Boundary Depth",
       x = "Total Inflow",
       y = "Channel Depth",
       col = "Date")

ggplotly(depth_scatter)

Exercise 3 - Mean depth line plot

mean_line <- flood_df %>%
  ggplot(mapping = aes(x = date,
                       y = mean_depth)) +
  geom_line(col = "blue") +
  labs(title = "Mean Flood Depth Over Time",
       x = "Time",
       y = "Mean Depth (m)")

ggplotly(mean_line)

Exercise 3 - Max depth line plot

max_line <- flood_df %>%
  ggplot(mapping = aes(x = date,
                       y = max_depth)) +
  geom_line(col = "blue") +
  labs(title = "Max Flood Depth Over Time",
       x = "Time",
       y = "Max Depth (m)")

ggplotly(max_line)

Exercise 3 - Total flooded area line plot

flood_line <- flood_df %>%
  mutate(total_area_km = total_area / 1000000) %>%
  ggplot(mapping = aes(x = date,
                       y = total_area_km)) +
  geom_line(col = "blue") +
  labs(title = "Total Area Flooded Over Time",
       x = "Time",
       y = "Flooded Area (km²)")

ggplotly(flood_line)

Exercise 4 - Buildings line plot

buildings_line <- hydros %>%
  ggplot(mapping = aes(x = DateTime,
                       y = buildings_flooded)) +
  geom_line(col = "blue") +
  labs(title = "Amount of Buildings Flooded Over Time",
       x = "Time",
       y = "Flooded Buildings")

ggplotly(buildings_line)